class_docstr: Concept Activation Vector (CAV) is a vector orthogonal to the decision
  boundary of a classifier which distinguishes between activation
  vectors produced by different concepts.
  More details can be found in the paper:
      https://arxiv.org/abs/1711.11279
docstr: Loads CAV dictionary from a pickle file for given input
  `layer` and `concepts`.
  
  Args:
      cavs_path (str): The root path where the cavs are stored
              in the storage (on the disk).
              Ex.: "/cavs"
      model_id (str): A unique model identifier associated with the
              CAVs. There exist a folder named `model_id` under
              `cavs_path` path. The CAVs are loaded from this folder.
      concepts (list[Concept]): A List of concepts for which
              we would like to load the cavs.
      layer (str): The layer name. Ex.: "inception4c". In case of nested
              layers we use dots to specify the depth / hierarchy.
              Ex.: "layer.sublayer.subsublayer"
  
  Returns:
      cav(CAV): An instance of a CAV class, containing the respective CAV
              score per concept and layer. An example of a path where the
              cavs are loaded from is:
              "/cavs/default_model_id/0-1-2-inception4c.pkl"
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code: def load(
      cavs_path: str, model_id: str, concepts: List[Concept], layer: str
  ):
      r"""
      Loads CAV dictionary from a pickle file for given input
      `layer` and `concepts`.
  
      Args:
          cavs_path (str): The root path where the cavs are stored
                  in the storage (on the disk).
                  Ex.: "/cavs"
          model_id (str): A unique model identifier associated with the
                  CAVs. There exist a folder named `model_id` under
                  `cavs_path` path. The CAVs are loaded from this folder.
          concepts (list[Concept]): A List of concepts for which
                  we would like to load the cavs.
          layer (str): The layer name. Ex.: "inception4c". In case of nested
                  layers we use dots to specify the depth / hierarchy.
                  Ex.: "layer.sublayer.subsublayer"
  
      Returns:
          cav(CAV): An instance of a CAV class, containing the respective CAV
                  score per concept and layer. An example of a path where the
                  cavs are loaded from is:
                  "/cavs/default_model_id/0-1-2-inception4c.pkl"
      """
  
      cavs_path = CAV.assemble_save_path(
          cavs_path, model_id, concepts, layer
      )
  
      if os.path.exists(cavs_path):
          save_dict = torch.load(cavs_path)
  
          concept_names = save_dict["concept_names"]
          concept_ids = save_dict["concept_ids"]
          concepts = [
              Concept(concept_id, concept_name, None)
              for concept_id, concept_name in zip(
                  concept_ids, concept_names
              )
          ]
          cav = CAV(concepts, save_dict["layer"], save_dict["stats"])
  
          return cav
  
      return None
  
file_path: captum/captum/concept/_core/cav.py
imports: import os
  from typing import Any, Dict, List
  import torch
  from captum.concept._core.concept import Concept
  from captum.concept._utils.common import concepts_to_str
label_desc: Use image node_id 5 for calling the CAV obj's underlying member method code with example usage: obj.load(cavs_path, model_id, concepts, layer) and returns: None, cav